class_docstr: Partitions a dataset into different chuncks. 
code: def __init__(self, data, sizes=[0.7, 0.2, 0.1], seed=1234):
      self.data = data
      self.partitions = []
      rng = Random()
      rng.seed(seed)
      data_len = len(data)
      indexes = [x for x in range(0, data_len)]
      rng.shuffle(indexes)
  
      for frac in sizes:
          part_len = int(frac * data_len)
          self.partitions.append(indexes[0:part_len])
          indexes = indexes[part_len:]
  
imports: import os
  import torch
  import torch.distributed
  import torch.nn
  import torch.nn.functional
  import torch.optim
  import nni
  import logging
  from math import ceil
  from random import Random
  from torch.autograd import Variable
  from torchvision import datasets, transforms
label_desc: Use image node_id 1 to create a new DataPartitioner object from inherited base classes: object with example: obj = DataPartitioner(data, sizes, seed)